Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
Querying local SQLite index...
iikit-clarify
by intent-integrity-chainResolve ambiguities in any project artifact — auto-detects the most recent artifact (spec, plan, checklist, testify, tasks, or constitution), asks targeted questions with option tables, and writes answers back into the artifact's Clarifications section. Use when requirements are unclear, a plan has trade-off gaps, checklist thresholds feel wrong, test scenarios are imprecise, task dependencies seem off, or constitution principles are vague.
iikit-00-constitution
by intent-integrity-chainCreate or update a CONSTITUTION.md that defines project governance — establishes coding standards, quality gates, TDD policy, review requirements, and non-negotiable development principles with versioned amendment tracking. Use when defining project rules, setting up coding standards, establishing quality gates, configuring TDD requirements, or creating non-negotiable development principles.
iikit-01-specify
by intent-integrity-chainCreate a feature specification from a natural language description — generates user stories with Given/When/Then scenarios, functional requirements (FR-XXX), success criteria, and a quality checklist. Use when starting a new feature, writing a PRD, defining user stories, capturing acceptance criteria, or documenting requirements for a product idea.
iikit-02-plan
by intent-integrity-chainGenerate a technical design document from a feature spec — selects frameworks, defines data models, produces API contracts, and creates a dependency-ordered implementation strategy. Use when planning how to build a feature, writing a technical design doc, choosing libraries, defining database schemas, or setting up Tessl tiles for runtime library knowledge.
iikit-03-checklist
by intent-integrity-chainGenerate quality checklists that validate requirements completeness, clarity, and consistency — produces scored checklist items linked to specific spec sections (FR-XXX, SC-XXX). Use when reviewing a spec for gaps, doing a requirements review, verifying PRD quality, auditing user stories and acceptance criteria, or gating before implementation.
iikit-04-testify
by intent-integrity-chainGenerate Gherkin .feature files from requirements before implementation — produces executable BDD scenarios with traceability tags, computes assertion integrity hashes, and locks acceptance criteria for test-driven development. Use when writing tests first, doing TDD, creating test cases from a spec, locking acceptance criteria, or setting up red-green-refactor with hash-verified assertions.
iikit-05-tasks
by intent-integrity-chainGenerate dependency-ordered task breakdown from plan and specification. Use when breaking features into implementable tasks, planning sprints, or creating work items with parallel markers.
iikit-06-analyze
by intent-integrity-chainValidate cross-artifact consistency — checks that every spec requirement traces to tasks, plan tech stack matches task file paths, and constitution principles are satisfied across all artifacts. Use when running a consistency check, verifying requirements traceability, detecting conflicts between design docs, or auditing alignment before implementation begins.
iikit-07-implement
by intent-integrity-chainExecute the implementation plan by coding each task from tasks.md — writes source files, runs tests, verifies assertion integrity, and validates output against constitutional principles. Use when ready to build a feature from a tasks.md plan, start coding against an Intent Integrity Kit implementation plan, develop from the task list, resume a partially completed implementation, or run the implement phase of the iikit workflow.
iikit-08-taskstoissues
by intent-integrity-chainConvert tasks from tasks.md into GitHub Issues with labels and dependencies. Use when exporting work items to GitHub, setting up project boards, or assigning tasks to team members.
iikit-bugfix
by intent-integrity-chainReport a bug against an existing feature — creates a structured bugs.md record, generates fix tasks in tasks.md, and optionally imports from or creates GitHub issues. Use when fixing a bug, reporting a defect, importing a GitHub issue into the workflow, or triaging an error without running the full specification process.
iikit-core
by intent-integrity-chainInitialize an IIKit (Intent Integrity Kit) project, uninit (remove IIKit scaffolding before `tessl uninstall`), check IIKit feature progress, select the active IIKit feature, and display the IIKit workflow command reference. Use when starting a new IIKit project, running IIKit init or setup, uninstalling/removing/uninit-ing IIKit before running `tessl uninstall`, checking IIKit status, switching between IIKit features, looking up IIKit available commands and phases, or asking for help with the IIKit workflow.
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.